JP2017190193A - Abnormality diagnosis device of system apparatus, abnormality diagnosis device of elevator and abnormality diagnosis method of elevator - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an abnormality diagnosis device of an elevator which can early specify whether an abnormal state occurring in an elevator is caused by a human factor, or a factor based on an element apparatus which constitutes the elevator.SOLUTION: The output information of sensors 1 to 3 for acquiring information on a behavior of a user of a diagnosed elevator EL1 that is a diagnosis object and the output information of a sensor which is the same as that of an elevator EL2 whose business form and usage are similar to those of the diagnosed elevator are compared with each other, the behavior information of the user of the diagnosed elevator is acquired, the output information of sensors 1 to 6 for acquiring information on an element apparatus constituting the diagnosed elevator EL1 and the output information of a sensor which is the same as that of the same element apparatus of the same kind of an elevator EL3 which is the same as the diagnosed elevator EL are compared with each other, variation information on a motion of the element apparatus of the diagnosed elevator is acquired, and it is determined whether or not an abnormal state occurs in the diagnosed elevator by a human factor from a correlation of the behavior information of the user and the variation information of the element apparatus.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a system device abnormality diagnosis device, an elevator abnormality diagnosis device, and an elevator abnormality diagnosis method, and more particularly, a system device abnormality diagnosis device, an elevator abnormality diagnosis device, and an elevator abnormality that can identify the cause of an abnormal state at an early stage. It relates to a diagnostic method.

  An elevator as a system device transports users and luggage by a car that moves up and down a hoistway formed in a building. In particular, many elevators are installed in large-scale buildings and the like, and many users use them. In recent elevators, the user himself / herself operates a landing button and a car button to call the car and to operate the car on the destination floor.

  For example, when a user gets in an elevator car, if the user presses the call button provided in the hall (elevator hall) on each floor, the user arrives at the boarding place where the car was called in response to this, and then the landing The door of the car and the door of the car open automatically, and the user can get into the car. In addition, when a user who has boarded the car presses the destination button in the car and designates the target floor, the landing door and the car door automatically close, and the car is directed to the target floor. Move up and down. Upon arrival at the target floor, the landing door and the car door automatically open, and the user moves from the car to the landing.

  As described above, in a large-scale building or the like, a plurality of elevators are installed and used by many users. Therefore, it is extremely important to operate and manage the elevators in a normal state. Therefore, in order to operate and manage the elevator in a normal state, a remote monitoring device that monitors the operation state of the elevator by remote operation is used.

  The remote monitoring device monitors various abnormal states that occur during the operation of the elevator. For this reason, the remote monitoring device is provided with an abnormality diagnosis device that performs abnormality diagnosis corresponding to the type of abnormal state. This abnormality diagnosis apparatus takes in detection signals of various sensors provided in the elevator and executes abnormality diagnosis by a predetermined abnormality diagnosis program.

  For example, Japanese Patent Application Laid-Open No. 2006-143359 (Patent Document 1) has an object of diagnosing the operating state of an elevator installed in a building to be diagnosed from operating histories of elevators installed in a plurality of buildings. The following abnormality diagnosis apparatus has been proposed.

  The abnormality diagnosis apparatus includes an installation data storage unit that stores installation data related to an elevator, a history data storage unit that stores elevator operation history data, and a similar building specifying unit (diagnosis target building data) that specifies a similar building from a plurality of buildings. Extraction section, search condition creation section, history data extraction section) and a diagnosis section for diagnosing the operating state of the elevator installed in the diagnosis target building, and the operation history data of the elevator installed in the diagnosis target building Relative evaluation is performed to diagnose the operating state of the elevator.

  Many other abnormality diagnosis apparatuses for remote monitoring apparatuses have been proposed, and Patent Document 1 described above shows an example thereof. The remote monitoring device is also in data communication with the control device installed in the car, and the output information of various sensors and the control information of the elevator are sent from the control device to the remote monitoring device. Backup information and notification information to be transmitted are sent to the control device. Furthermore, the information on the landing is also sent to the remote monitoring device and used for elevator operation and management.

JP 2006-143359 A

  In the control device installed in the elevator car, for example, the presence / absence of the user, the time when the user got into the car, etc. are detected by various sensors provided at the landing and the car and are grasped as log information. Has been. For example, it is possible to detect the user's access time and the presence / absence of the user by using an image of a sensor camera attached to the car, a door sensor, and a load sensor. It should be noted that information on the user can be detected using sensors other than these, and various information other than the presence / absence of the user and the entry / exit time can also be detected.

  When the presence of a user is confirmed, the doors that normally close after a predetermined time (the landing and the door of the car) are kept open so that users entering and leaving the car are not caught by the door. I have to. Further, when the user suddenly enters and exits in the process of closing the door, an operation of opening the closed door again is executed. In this way, the elevator platform and the car use many various sensors to monitor the behavior of the user and control the operation of the door so that the user is not caught by the door. .

  By the way, the event that the user collides with the door due to the time delay until the door is opened again after the user is detected by various sensors, when the user forcibly passes the closing door, Events that collide with the door, and events such as a user jokingly jumping in the car and colliding with the door often occur. If this user collides with the door and the force acting on the door is large, the opening and closing of the door will be adversely affected.

  In many cases, even if the user collides with the door, there is almost no influence on the door. However, in some cases, the door may temporarily stop operating smoothly. That is, a temporary abnormal state based on an artificial cause may occur. In such a state, the switches and sensors that detect the open / closed state of the door detect that the open / closed state of the door is different from the normal state, so the abnormality diagnosis device determines that an abnormality has occurred. An alarm is notified to the user, and in some cases, an operation to stop the operation of the elevator is executed to ensure safety.

  When such an abnormal condition occurs, the maintenance worker of the operator operating and managing the elevator visits the site where the abnormal condition occurs and confirms the abnormal condition to determine the cause of the abnormal condition. After eliminating, the elevator is restored. However, before the maintenance worker arrives at the site, the door may return to a normal state due to some kind of trigger (for example, when the user corrects the distortion or position of the door). Therefore, in such a state, when the maintenance worker arrives at the site, the abnormal state is resolved, so it takes a lot of time to find the true cause determined to be the abnormal state.

  As a similar example, for example, when a foreign object is placed on the rail part or the drive part of the door (including intentional or unintentional cases), the abnormality diagnosis device assumes that the door is not completely closed. Judge that an abnormal condition has occurred in the door. That is, a temporary abnormal state based on an artificial cause occurs. However, if the user or the personnel of the business operator who performs the maintenance of the building removes the foreign matter, the door returns to perform a normal operation.

  Even in such a state, since the abnormal state is resolved when the maintenance worker arrives at the site, it takes a lot of time to determine the cause of determining the abnormal state. The occurrence of a temporary abnormal state based on an artificial cause is caused by many causes other than the above-described example.

  On the other hand, apart from the abnormal state due to human causes, the component devices constituting the elevator are abnormal due to the length of time used and various physical factors (mechanical and electrical factors). For example, an abnormal state of element devices due to deterioration with time occurs relatively frequently. Although this abnormal state is detected by various sensors, the output information of the various sensors cannot determine whether the abnormal state is based on an artificial cause or an abnormal state based on the cause of an element device.

  Regardless of the abnormal state based on human causes or the abnormal state based on the cause of elemental equipment, the maintenance worker must stop the elevator and investigate in order to find out the cause of the abnormal state. In addition, the elevator cannot be used during the investigation period by the maintenance worker. Therefore, in order to improve convenience for the user, it is desirable to find out the cause of the abnormal state at an early stage.

  The same problem may occur not only in the elevator but also in various system devices. Therefore, in the embodiment described below, an example of an elevator will be described as a typical example, but the present invention can also be applied to other system devices. Therefore, in the following description, including these, what is described as an elevator can be read as a system device.

  An object of the present invention is that an abnormal state generated in an elevator (system device) is an abnormal state generated by an artificial cause or an abnormal state generated due to an element device constituting the elevator (system device) It is an object of the present invention to provide a novel elevator abnormality diagnosis device (system device abnormality diagnosis device) and an elevator abnormality diagnosis method that can identify whether the vehicle is early.

  The features of the present invention are the same as the output information of the behavior sensor that acquires information on the behavior of the user of the elevator to be diagnosed (diagnosis system device) to be diagnosed and the behavior sensor of the elevator to be diagnosed (diagnosis system device) The behavior information of the user of the elevator to be diagnosed (diagnosed system device) is obtained by comparing the output information of the behavior sensor of the first comparative elevator (system device for first comparison) with similar business conditions and uses, and , Elevator to be diagnosed (diagnosed) with the same output information of element device sensor that obtains information on element devices that make up diagnosed elevator (diagnostic system device) Sensor device sensor output of the second comparison elevator (second comparison system device) of the same model as the system device) Compared with the information, the fluctuation information regarding the operation of the component equipment of the elevator to be diagnosed (diagnosed system equipment) is obtained, and the elevator to be diagnosed (diagnostic system equipment) from the correlation between the behavior information of the user and the fluctuation information of the element equipment ) To determine whether an abnormal condition has occurred due to human causes.

  According to the present invention, the abnormal state occurring in the diagnosed elevator (diagnosed system device) is an abnormal state caused by human factors or based on the component devices of the diagnosed elevator (diagnosed system device). Whether or not an abnormal state has occurred due to the cause can be identified at an early stage, and the stop period of the diagnosed elevator (diagnostic system device) can be shortened to improve user convenience.

It is a block diagram of the abnormality diagnosis apparatus of the elevator which becomes embodiment of this invention. It is explanatory drawing which shows the output information of the sensor regarding a user, and the output information of the sensor regarding an element apparatus, and demonstrates the case where a user's behavior is normal. It is explanatory drawing which shows the output information of the sensor regarding a user, and the output information of the sensor regarding an element apparatus, and demonstrates the case where a user's behavior is abnormal. It is explanatory drawing explaining the identification method of the abnormal state by the correlation of the output information of the abnormal operation | movement detection means which is a behavior detection means, and the fluctuation | variation information detection means. It is a block diagram of the abnormality diagnosis apparatus of the elevator which becomes other embodiment of this invention.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the present invention is not limited to the following embodiments, and various modifications and application examples are included in the technical concept of the present invention. Is also included in the range. In addition, as above-mentioned, although this invention is applicable not only to an elevator but various system apparatuses, the elevator is demonstrated as a typical example below.

  FIG. 1 shows a configuration of an abnormality diagnosis apparatus for an elevator according to an embodiment of the present invention. In FIG. 1, a person for determining whether or not a user is present in an open / close area of a door in a car (not shown) of an elevator (not shown) EL1 to be diagnosed (hereinafter referred to as an elevator to be diagnosed). Sensing sensor 1, image sensor 2 such as a monitoring camera or a sensor camera provided in the car of the diagnosed elevator EL1, and a load sensor that measures the weight of the user and the luggage that are in the car of the diagnosed elevator EL1 3 is provided. Of course, it is possible to provide sensors other than these, and a door sensor 4 for detecting the opening and closing of the door, and sensors A5 and B6 other than this are provided.

  Then, for example, the output information of the human sensor 1, the image sensor 2, and the load sensor 3 which are behavior sensors for detecting the behavior of the user is sent to the abnormal operation detection means 7 which is the behavior detection means as the behavior information of the user. Have been entered. (Hereinafter, the behavior detection means is referred to as abnormal operation detection means 7.) The output information input to the abnormal operation detection means 7 is handled as behavior information representing the user's behavior and the like. Note that the output information of the behavior sensor representing the behavior information of the user can be used in addition to the above-described sensor, and the information may be any information that represents the behavior of the user. Such user behavior information is used in order to determine whether or not an artificial abnormal state has been caused by the user.

  Further, the behavior information for comparison is inputted to the abnormal operation detecting means 7, and this behavior information for comparison forms a threshold value to be compared with the behavior information of the diagnosed elevator EL1. The comparison behavior information is obtained by the first first comparison elevator EL2. The first comparison elevator EL2 is an elevator different from the diagnosed elevator EL1, and is an elevator having similar business conditions and uses in which the diagnosed elevator EL1 is used.

  For example, if the diagnosed elevator EL1 is a department store elevator, the first comparison elevator EL2 is also a department store elevator. If the diagnosed elevator EL1 is a common apartment elevator, the first comparison elevator EL2 is also a common apartment elevator. If it is an elevator and the diagnosed elevator EL1 is an office building elevator, the first comparative elevator EL2 is also an office building elevator. There are other elevators with similar business conditions and uses, but they are omitted here.

  Thus, the reason for using a comparative elevator with similar business conditions and uses is that it can be considered that the user of the diagnosed elevator EL1 and the user of the first comparative elevator EL2 have a high probability of performing the same behavior. Because.

  Of the sensor group 8 of the first comparison elevator EL2, the same human sensor, image sensor, and load sensor as the human sensor 1, image sensor 2, and load sensor 3 used in the elevator EL1 to be diagnosed. The output information is input to the normal behavior information storage unit 9 as normal behavior information. The normal behavior information storage means 9 is input every predetermined time. The normal behavior information storage means 9 may input the output information of the human sensor, the image sensor, and the load sensor as the behavior information for comparison as it is to the abnormal operation detection means 7, or the output information of each sensor may be subjected to a predetermined calculation. And may be input to the abnormal operation detection means 7 as comparison behavior information.

  In the abnormal operation detecting means 7, behavior information is formed by information from the human sensor 1, the image sensor 2 and the load sensor 3 of the diagnosed elevator EL1, and the behavior information of the diagnosed elevator EL1 is the first comparison elevator. Comparison with the behavior information for comparison in a predetermined range formed by information from the human sensor, the image sensor, and the load sensor of EL2. For example, if the behavior information of the diagnosis elevator EL1 is within a predetermined range with respect to the comparison behavior information of the first comparison elevator EL2, the behavior information of the diagnosis elevator EL1 is in a normal state and exceeds the predetermined range. If it has changed, it is assumed that the change in the behavior information of the diagnosed elevator EL1 is suspected of being caused by an artificial cause.

  In the present embodiment, the “probability” in which an abnormal state has occurred due to an artificial cause according to the degree to which the value of the behavior information of the diagnosed elevator EL1 exceeds the value of the comparison behavior information. Is judged to be high. That is, as the value of the behavior information of the diagnosis elevator EL1 becomes larger than the average value (or the median value of the predetermined range) of the comparison behavior information of the first comparison elevator EL2, the abnormal state is caused by an artificial cause. “Probability” in which the occurrence of the error is set to be high.

  On the other hand, output information of, for example, the human sensor 1, the image sensor 2, the load sensor 3, the door sensor 4, the sensor A5, and the sensor B6, which are element device sensors of the diagnosis elevator EL1, is input to the fluctuation information detection unit 10. ing. The element device sensor and the behavior sensor include the case where they are the same sensor. The output information input to the fluctuation information detecting means 10 is handled as time-dependent deterioration information indicating deterioration with time (cause based on the element devices) of the element devices constituting the diagnosed elevator EL1. Further, the time-dependent deterioration information can be regarded as variation information regarding the operation of the element device.

  In addition, the output information of the element device sensor representing the time-dependent deterioration information can be used in addition to the above-described sensor. However, in the present embodiment, the abnormal state of the element device is described using the variation information due to deterioration over time as the variation information of the element device. However, this is caused by other causes, for example, mechanical or electrical action. Needless to say, the abnormal state of the element device may be determined using the variation information regarding the operation of the element device.

  Here, the element equipment is, for example, a door constituting a car, a door opening / closing mechanism, a door drive motor, a hoisting machine that winds up the car, a braking device, and the like. Here, the door of the car is opened and closed. As an example, a device is described.

  Further, the temporal deterioration information for comparison is input to the fluctuation information detecting means 10, and this comparative temporal deterioration information forms a threshold value to be compared with the temporal deterioration information of the diagnosed elevator EL1. The comparative deterioration with time information is obtained by the second comparative elevator EL3. The second comparison elevator EL3 is an elevator different from the diagnosed elevator EL1, and is an elevator of the same model (same type) as the diagnosed elevator EL1.

  In this way, as the second comparison elevator EL3, the elevator of the same model as the diagnosed elevator is used because the element devices of the diagnosed elevator EL1 and the second comparison elevator EL3 are the same. This is because it can be considered that there is a high probability that the tendency of deterioration with time of the equipment is the same.

  In the sensor group 11 of the second comparison elevator EL3, the same sensor as the human sensor 1, the image sensor 2, the load sensor 3, the door sensor 4, the sensor A5, and the sensor B6 used in the elevator EL1 to be diagnosed. The output information of the human sensor, the image sensor, the load sensor, the door sensor, the sensor A, and the sensor B is input to the temporal deterioration information storage unit 12 as fluctuations with the passage of time (deterioration with time).

  The time degradation information storage unit 12 is input every predetermined time. The temporal deterioration information storage means 12 generates comparative temporal deterioration information from the output information of the human sensor, image sensor, load sensor, door sensor, sensor A, and sensor B, and is input to the fluctuation information detection means 10 as it is. ing.

  Whether deterioration with time has occurred is determined, for example, by determining the amount of change (deviation) in the output information every predetermined time under the same environmental conditions, and using it as deterioration information over time. Can be sought. In addition to this, the tendency of deterioration with time can be grasped by various methods, but a method suitable for the system may be adopted.

  In the fluctuation information detecting means 10, the time-dependent deterioration information is formed by information from various sensors of the diagnosed elevator EL1, and the time-dependent deterioration information of the diagnosed elevator EL1 is determined by information from various sensors of the second comparison elevator EL3. It compares with the time-dependent deterioration information for comparison of the formed predetermined range. For example, if the time-lapse deterioration information of the diagnosed elevator EL1 is within a predetermined range with respect to the comparison time-lapse deterioration information of the second comparison elevator EL3, the time-lapse deterioration information of the diagnosis elevator EL1 is in a normal state, If it fluctuates beyond the range, it is considered that the change in the deterioration information of the diagnosed elevator EL1 with time is caused by the deterioration of the element equipment.

  In the present embodiment, an abnormal state occurs due to the deterioration of the element device according to the degree to which the value of the time-lapse deterioration information of the diagnostic elevator EL1 exceeds the value of the comparison time-lapse deterioration information. Judged that the "probability" is high. That is, as the value of the time degradation information of the diagnosis elevator EL1 becomes larger than the average value (or the median value of the predetermined range) of the comparison time degradation information of the second comparison elevator EL3, the deterioration of the element equipment is reduced. “Probability” that an abnormal condition has occurred due to the cause is set to be high.

  The behavior information related to the user's behavior determined by the abnormal operation detection unit 6 and the temporal deterioration information by the fluctuation information detection unit 10 are input to the cause determination unit 13. In the cause determination means 13, it is determined whether the variation in the output information of each sensor is due to deterioration with time of the element device or due to abnormal behavior of the user.

  Based on the correlation between the behavior information related to the user's behavior and the time-dependent deterioration information of the component devices, the cause determination means 13 determines whether the abnormal state that has occurred in the diagnosed elevator EL1 is an abnormal state that has occurred artificially or It is judged whether it is the abnormal state which arose based on the cause based on the element equipment of a diagnostic elevator. The determination of an abnormal state based on this correlation will be described with reference to FIG.

  When the abnormal state is determined by the cause determining means 13, the determination result is sent to the output device 14. The output device 14 notifies at least the determination result of the cause determination means 13 by a display screen or voice, but can also display the output information of the abnormal operation detection means 7 and the fluctuation information detection means 10 in time series. It is configured as follows. In this case, the output information of the abnormal operation detecting means 6 and the fluctuation information detecting means 10 may be stored in time series every predetermined time.

  Next, specific processing of the abnormal operation detection unit 6, the fluctuation information detection unit 10, and the cause determination unit 13 will be described. FIG. 2 shows sensor output information related to user behavior and sensor output information related to aging of elemental devices, and illustrates the case where the user behavior is normal. FIG. 3 relates to user behavior. The sensor output information and sensor output information regarding the aging of the component devices are shown, and the case where the user's behavior is abnormal is described. FIG. 4 illustrates a method for identifying an abnormal state based on correlation of output information from the abnormal operation detection means 6 and the fluctuation information detection means 10.

  FIG. 2 shows the sensor output information related to the user's behavior and the sensor output information related to the aging of the component devices as described above, and explains the case where the user's behavior is normal.

  In FIG. 2, the output characteristics shown on the upper side show the fluctuation state of the output information (load) of the load sensor 3 provided in the car of the elevator EL1 for diagnosis. The load sensor 3 is used to determine whether or not an abnormal state has occurred due to an artificial cause. Time t0 to time t1, time t2 to time t3, and time t4 to time t5 indicate periods from when the car stops at a predetermined floor and the car is opened and closed again, and time t1 to time t2 , Time t3 to time t4, and time t5 indicate an operation period in which the car is operated to move on the hoistway.

  Then, at time t0 to time t1, time t2 to time t3, and time t4 to time t5, the passenger's door is opened and closed, the user gets on and off, and finally the number of users present in the passenger car is the total weight. It is measured by the load sensor 3. In this state, the total weight of the same number of persons in the first comparison elevator EL2 is used as a threshold for comparison, and is compared with the output information of the load sensor 3 of the diagnosis elevator EL1 by the abnormal operation detection means 7. The output information of the load sensor 3 of the first comparison elevator EL2 may use an average value of the output information of the load sensors 3 of the plurality of first comparison elevators EL2.

  The threshold value by the load sensor of the first comparison elevator EL2 has a predetermined range. If the output information of the load sensor 3 of the elevator EL1 for diagnosis falls within this range, the abnormal operation detection means 7 is used. The person has no abnormal behavior. The abnormal behavior will be described with reference to FIG. In FIG. 2, the operation period from time t1 to time t2 falls within the range of the threshold value WSL1, and the operation period from time t3 to time t4 also falls within the range of the threshold value WSL2. It is assumed that this has not occurred.

  Here, although the load sensor 3 is used as a behavior sensor in the present embodiment, the sensor camera 2 can be used instead. In this case, an abnormality in the user's behavior can be detected by measuring the user's motion position, motion speed difference, and motion time difference in the image. Furthermore, it is possible to detect an abnormality in the user's behavior based on the user's operation pattern and the change history of the pattern.

  Next, the output characteristics shown on the lower side show the fluctuation state of the output information of the door sensor 4 provided on the car of the elevator EL1 for diagnosis. The door sensor 4 detects the opening / closing speed of a car door which is an element of the elevator EL1 for diagnosis, and an abnormal state due to deterioration with time occurs in the car door due to temporal fluctuation of the opening / closing speed. It is determined whether or not. Here, the opening / closing speed of the door is obtained from an average speed when the door is in the open state from the closed state and when the door is in the open state from the open state.

  Then, at time t0 to time t1, time t2 to time t3, and time t4 to time t5, both average speeds when the door is changed from the closed state to the open state and when the door is changed from the open state to the closed state are calculated. doing. And the difference value (DELTA) V (= time-dependent deterioration information) of the average opening / closing speed measured at the predetermined time and the average opening / closing speed measured at the predetermined time when the subsequent predetermined time has passed is obtained. It is considered that the influence of deterioration over time is large, and there is a high possibility of becoming an abnormal state. Of course, in the second comparison elevator EL3, the same calculation is executed and stored corresponding to the opening and closing of the door of the car.

  In this state, the difference value ΔVSL of the average opening / closing speed of the door obtained by the door sensor 4 of the second comparison elevator EL3 (the elapsed time of the predetermined time before and after obtaining the difference value is the same time) is for comparison. It is used as a threshold value and is compared with the difference value ΔV of the average opening / closing speed of the door obtained by the door sensor 4 of the diagnostic elevator EL1 by the fluctuation information detecting means 10. Since the second comparison elevator EL3 is of the same model as the diagnosed elevator EL1, the degree of progress of deterioration over time shows almost the same tendency. Therefore, if the difference value between the two is compared, an abnormal state based on deterioration over time is estimated. be able to.

  When selecting the second comparison elevator EL3, it is desirable to select the second comparison elevator EL3 having a new installation date and time. As a result, the detection probability of deterioration over time of the elevator EL1 for diagnosis is increased. Further, as the time-dependent deterioration information (difference value) of the door sensor of the second comparison elevator EL3, an average value of the time-dependent deterioration information of the door sensors 3 of the plurality of second comparison elevators EL3 may be used.

  The threshold value (difference value ΔVSL) by the door sensor of the second comparison elevator EL3 has a predetermined range, and the temporal deterioration information (difference value) of the door sensor 4 of the elevator EL1 for diagnosis falls within this predetermined range. If so, the fluctuation information detecting means 10 assumes that an abnormal state due to deterioration with time has not occurred in the door which is an element device. In the example shown in FIG. 2, a difference value ΔV1 is generated between the average opening / closing speed at time t0 to time t1 and the average opening / closing speed at time t2 to time t3. Similarly, the average opening / closing at time t0 to time t1 is generated. A difference value of ΔV2 is generated between the speed and the average opening / closing speed from time t4 to time t5, and represents a tendency that the door gradually becomes difficult to close due to deterioration over time.

  Therefore, the deterioration with time (difference value) ΔV1 of the door sensor 3 of the diagnosed elevator EL1 from the threshold (difference value ΔVSL) in a predetermined range determined by the deterioration with time (difference value) of the door sensor of the second comparison elevator EL3. Alternatively, if the value of ΔV2 is exceeded, it can be estimated that an abnormal state has occurred in the door, which is an element device, or is in a state where it is likely to occur. Here, the larger the difference value is, the more the door is deteriorated with time, and the “probability” that the door is in an abnormal state is set higher.

  Then, based on the behavior information of the user of the abnormal operation detecting means 7 and the time-dependent deterioration information of the fluctuation information detecting means 10, the cause determining means 13 determines that the abnormal state that has occurred in the elevator is an abnormal state that has been caused by an artificial cause. It can be identified at an early stage whether there is an abnormal state caused by the cause based on the component devices constituting the elevator. Since the abnormal state shown in FIG. 2 is not considered to be an abnormal state caused by an artificial cause by the abnormal operation detecting means 7, the cause determining means 13 is the abnormal state caused by the cause based on the element device. Judgment that is considered to be is executed. This determination result can be easily confirmed by the output device 14.

  Next, a case where the user performs an abnormal behavior will be described. As described above, FIG. 3 shows sensor output information related to user behavior and sensor output information related to the deterioration of elemental devices, and describes a case where the user behavior is abnormal.

  In FIG. 3, the output characteristics shown on the upper side show the variation state of the output information (load) of the load sensor 3 provided in the car of the elevator for diagnosis EL1, similarly to FIG. The load sensor 3 is used to determine whether or not an abnormal state has occurred due to an artificial cause as described above. Time t0 to time t1, time t2 to time t3, time t4 to time t5, and time t6 to time t7 indicate periods until the car stops at a predetermined floor and the car opens and closes again. , Time t1 to time t2, time t3 to time t4, time t5 to time t6, and time t7 to indicate an operation period in which the car is operated to move on the hoistway.

  Then, at time t0 to time t1, time t2 to time t3, time t4 to time t5, and time t6 to time t7, the user gets on and off, and finally the number of users present in the car is the total weight. It is measured by the load sensor 3. In this state, the total weight of the same number of persons in the first comparison elevator EL2 is used as a threshold for comparison, and is compared with the output information of the load sensor 3 of the diagnosis elevator EL1 by the abnormal operation detection means 7. This state is the same as that shown in FIG.

  However, if the user performs an abnormal behavior such as jumping in the car during the operation period from time t3 to time t4, the load sensor 3 detects large output information (load) Abw in response thereto. . This output information Abw is larger than the threshold value WSL2 of the output information (load) of the load sensor 3 of the first comparison elevator EL2. For this reason, the abnormal operation detecting means 7 considers that an abnormal state due to an artificial cause has occurred, assuming that an abnormal behavior of the user has occurred during the operation period from time t3 to time t4. As described above, the greater the magnitude of the output information Abw from the load sensor 3, the higher the “probability” of the determination that the abnormal behavior of the user is occurring.

  As described above, since the first comparison elevator EL2 similar in business condition and application is used to the diagnosis elevator EL1, the user of the diagnosis elevator EL1 and the first comparison elevator EL2 are generally used. The probability that the user will perform the same behavior is high. For this reason, although the ratio of the abnormal state detected by the user is usually low, the abnormal state is caused by a user who is different from the user of the elevator EL1 for diagnosis and the first comparison elevator EL2 whose business condition and use are similar. If there is a behavior that occurs, it is assumed that an abnormal state has occurred by the user.

  Next, the output characteristic shown on the lower side also shows the fluctuation state of the time-dependent deterioration information of the door sensor 4 provided in the car of the elevator for diagnosis EL1 as in FIG. The door sensor 4 detects the opening / closing speed of a car door which is an element of the elevator EL1 for diagnosis, and an abnormal state due to deterioration with time occurs in the car door due to temporal fluctuation of the opening / closing speed. It is determined whether or not. Here, the opening / closing speed of the door is calculated from the average speeds when the door is in the open state from the closed state and when the door is in the closed state.

  Then, at time t0 to time t1, time t2 to time t3, time t4 to time t5, and time t6 to time t7, when the door changes from the closed state to the open state and when the door changes from the open state to the closed state. Both average speeds are calculated. And, the average opening / closing speed measured at a predetermined time and the difference value between the average opening / closing speed measured at a predetermined time when a predetermined time thereafter has passed, the larger the difference value, the greater the influence of deterioration over time, We assume that there is a high probability of an abnormal condition.

  In this state, the difference value of the average door opening / closing speed obtained by the door sensor 4 of the second comparison elevator EL3 (the elapsed time of the predetermined time before and after obtaining the difference value is the same time) is a threshold value for comparison. The fluctuation information detecting means 10 compares the deterioration information (difference value) with time of the door sensor 4 of the elevator EL1 for diagnosis. This state is the same as that shown in FIG. However, in the case of FIG. 3, it is assumed that there is no abnormality due to deterioration over time. Therefore, there is almost no difference value between the average opening / closing speed between time t0 and time t1 and the average opening / closing speed between time t2 and time t3, and no abnormal state occurs due to deterioration with time.

  However, during the operation period from time t3 to time t4, the user may perform an abnormal behavior such as jumping in the car, and the user may give a large impact to the car door. For this reason, a door will be in the state different from a regular attachment state, and will come to interfere with the operation | movement. Therefore, after the operation period from time t3 to time t4 when there was an abnormal behavior of the user, between the average opening / closing speed of the door at time t4 to time 5 and the average opening / closing speed at time t0 to time t1. A difference value of ΔV3 is generated, and it can be determined that the door is difficult to close due to an abnormal behavior of the user.

  Therefore, when abnormal output information Abw from the load sensor 3 is detected, a diagnosis is made based on a threshold (difference value ΔVSL) in a predetermined range determined by the time-dependent deterioration information (difference value) of the door sensor of the second comparison elevator EL3. If the time-dependent deterioration information (difference value) ΔV3 of the door sensor 3 of the elevator EL1 exceeds, it can be determined that an abnormal state has occurred in the door due to an artificial cause, or that it is likely to occur.

  Then, based on the behavior information of the user of the abnormal operation detecting means 7 and the time-dependent deterioration information of the fluctuation information detecting means 10, the cause determining means 13 determines that the abnormal state that has occurred in the elevator is an abnormal state that has been caused by an artificial cause. It is possible to identify at an early stage whether there is an abnormal state caused by the cause based on the component devices constituting the elevator. Since the abnormal state shown in FIG. 3 is regarded as an abnormal state caused by an artificial cause by the abnormal operation detecting means 7, the cause determining means 13 is regarded as an abnormal state based on the artificial cause. Execute the judgment. This determination result can be easily confirmed by the output device 14.

  The average opening / closing speed of the door from time t6 to time t7 has a difference value of ΔV4 between the average opening / closing speed from time t0 to time t1. As seen from the relationship between the output information (load) of the load sensor 3 and the average opening / closing speed of the door as shown in FIG. 2, it is considered that the door sensor 4 is deteriorated with time.

  In reality, however, in this example, no abnormality due to deterioration with time has occurred, so the change in the average opening / closing speed of the door from time t6 to time t7 is based on an artificial cause that occurred during the operation period from time t3 to time t4. Since it is known that the state is an abnormal state, the change in the average opening / closing speed of the door from time t6 to time t7 is not a deterioration with time, but an abnormal state based on an artificial cause continues using this information. Can be determined.

  Here, the cause determination means 13 may employ the following determination method. In other words, if an abnormality of an element device is detected within a predetermined time after detecting an abnormal behavior of the user, if the abnormality of the element device generated at this time is an abnormal state caused by an artificial cause, The probability of determining a typical abnormal state can be further increased. Further, if an abnormal state of the element device is detected after the predetermined time has elapsed, this abnormal state can be determined as a true abnormal state of the element device.

  Next, a description will be given of a method for identifying by the cause determination means 13 whether an abnormal state has occurred due to an artificial cause or an abnormal state has occurred due to an element device constituting the elevator. In FIG. 4, the horizontal axis indicates the fluctuation information of the fluctuation information detection means 10, and the vertical axis indicates the behavior information of the abnormal operation detection means 7.

  As described above, the greater the behavior information (the load difference value) of the abnormal operation detection means 7, the higher the “probability” that an abnormal state has occurred due to an artificial cause. Similarly, “probability” that an abnormal state has occurred due to deterioration with time of an element device is set higher as the deterioration information (open / close speed difference value) of the fluctuation information detection means 10 is larger.

  “Black circle” in the figure indicates the correlation between the behavior information of the abnormal operation detecting means 7 and the time-dependent deterioration information of the fluctuation information detecting means 10, and the value of a certain time-dependent deterioration information of the fluctuation information detecting means 10 at the same time. The value of the behavior information of the abnormal operation detecting means 7 at the time is shown. A range surrounded by a broken line indicates a range in which each correlation is recognized, and these correlations are exemplarily shown as “state A” to “state D”.

  In FIG. 4, the variation information has an area division point P, and if it is smaller than the area division point P, it is considered that the deterioration with time is small and no abnormal state occurs. It is assumed that a condition occurs. Similarly, the behavior information has an area division point Q. If the behavior information is smaller than the area division point Q, it is considered that an abnormal state does not occur due to an artificial behavior by the user. It is assumed that an abnormal state occurs due to an artificial abnormal behavior caused by.

  Then, four regions are defined and formed by the region partition point P and the region partition point Q. (1) An Nr region that is a normal region in which the variation information is smaller than the region partition point P and the abnormal behavior information is smaller than the region partition point Q. (2) Abn1 region, which is an abnormal region, in which the variation information is larger than the region partition point P and the abnormal behavior information is smaller than the region partition point Q, and (3) the abnormal behavior information is smaller than the region partition point P. Abn2 region, which is an abnormal region, is larger than region partition point Q, and (4) Abn3 region, which is an abnormal region, whose variation information is larger than region partition point P and whose abnormal behavior information is larger than region partition point Q is formed. ing.

  “State A” belongs to the Nr area, and the diagnosed elevator EL1 is normally used. The abnormal behavior information and the fluctuation information are both set to predetermined values below the area partition points P and Q. It has become. This is, for example, the state from time t0 to time t1 in FIGS.

  In contrast to this “state A”, “state B” belongs to the Abn1 region, the variation information is larger than the region partition point P, and the abnormal behavior information is smaller than the region partition point Q. This “state B” is a result of the deterioration with time shown in FIG. 2 and is expressed by transition from “state A” to “state B”. However, in this case, the state does not change from “state B” to “state A”, and is determined to be a true abnormal state.

  In contrast to “state A”, “state C” belongs to the Abn2 region, the variation information is smaller than the region partition point P, and the abnormal behavior information is larger than the region partition point Q. This “state C” is a result of an abnormal state caused by the artificial behavior shown in FIG. 3, and is expressed by transition from “state A” to “state C”. In this state, deterioration with time does not occur. By shifting from the “state A” to the “state C”, it can be determined that an abnormal behavior has been performed by the user. In this case, if the abnormal state due to the artificial behavior is resolved, the state changes from “state C” to “state A”.

  In contrast to the “state A”, the “state D” belongs to the Abn3 region, the variation information is larger than the region partition point P, and the abnormal behavior information is larger than the region partition point Q. In this “state D”, an abnormal state is caused by the artificial behavior shown in FIG. 3, and an abnormal state of deterioration with time is caused by this artificial behavior. From “state A” to “state D” It is expressed by transitioning to. In this state, since it is determined that an abnormal state has occurred due to an artificial behavior, it is considered that deterioration with time does not occur. By shifting from the “state A” to the “state D”, it is possible to determine that an abnormal behavior is performed by the user, and as a result, temporal deterioration has occurred temporarily. Note that “state D” transitions to “state A” or “state B” when the artificial abnormal state is resolved.

  In FIG. 4, in “state B” and “state D”, since the variation information indicates values different from the normal state, it is determined that appropriate inspection and maintenance are necessary. However, in the “state D”, the output of the behavior information is large due to the abnormal behavior of the user, and therefore it is considered that the user is the main cause of the large variation information.

  In addition, since abnormal behavior by the user is not usually continued continuously, there is a possibility that the state is “state A” or “state B” at the time of inspection by the maintenance worker. Therefore, it is important to determine what trajectory has passed in FIG. 4 before reaching the “state B” which is a true abnormal state.

  A method for determining this will be described. When “state B” is reached, it is important how much “state D” and “state C” have been passed so far. The number of times of “state D” was Nd and “state C”. The number of times is Nc, and the time t at which “state D” is reached is ti and the time at which “state C” is reached is tj with respect to time t at which “state B” is reached. Also, the output of behavior information of “state D” is yd (ti), and the output of fluctuation information of “state C” is yc (tj). At this time,

Calculate Here, R (t) is an operation for determining the section of the region as shown in FIG. 4, for example, and determines the correlation state.

  Note that f (t) is a function that monotonously decreases as t increases.

As shown, the function asymptotically approaches “0”, or a function that becomes “0” when t is equal to or greater than a certain value. Based on the magnitude of R (t) calculated in this way, it can be determined whether or not there is a high possibility of an abnormal state based on an artificial behavior.

  In FIG. 4, four groups from “state A” to “state D” are shown. However, the number of groups for determination is not limited to four, and may be set appropriately according to each elevator. is there.

  Further, the function shown in the equation (1) may be any function that can evaluate what state has been reached before reaching the “state B”, for example, “state D” or “state C” at the end. The elapsed time after becoming “state B” or the number of times “state D” or “state C” until reaching “state B” may be used.

  Next, a second embodiment of the present invention will be described with reference to FIG. The second embodiment is different in that an acceleration sensor 15 is used instead of the load sensor 3 in order to detect an abnormal state caused by an artificial abnormal behavior by a user.

  In FIG. 5, the overall configuration is the same as that of the first embodiment, but the acceleration sensor 15 is provided at the bottom of the car. Accordingly, when acceleration occurs in the car due to the jumping of the user in the car, the behavior of the user can be detected by the acceleration sensor 15. The output information of the acceleration sensor 15 is sent to the abnormal operation detecting means 7, and the abnormal operation detecting means 7 can determine whether or not the abnormal state is caused by the abnormal behavior of the user.

  Further, if the acceleration sensor 15 is attached to the door of the car, it can be detected that the user has collided with the door. As a result, it is possible to increase the detection accuracy of whether or not the user has performed an abnormal behavior more directly. Generally, the user does not intentionally impact the door, and when the impact on the door is detected by the acceleration sensor 15 provided on the door, it is determined that an abnormal state has occurred due to the abnormal behavior of the user. can do.

  In addition to this, when the behavior information of the user can be obtained from the portable sensor device owned by the user, the behavior of the user can be detected using the behavior information. Further, if the user's behavior can be detected, the acceleration sensor 15 may not be used. For example, a signal from a position sensor provided in the hoistway may be used.

  In the present embodiment described above, the abnormal state of the element device is described using the variation information due to deterioration over time as the variation information of the element device. However, other causes such as physical and electrical It goes without saying that the abnormal state of the element device may be determined using the variation information of the element device resulting from the action.

  As described above, according to the present invention, output information of a behavior sensor that acquires information on the behavior of a user of a diagnosis target elevator (diagnosis system device) to be diagnosed, and a diagnosis elevator (diagnosis system device) The behavior of the user of the elevator to be diagnosed (diagnosed system device) by comparing with the output information of the behavior sensor of the first comparative elevator (first comparison system device) that is the same as the behavior sensor and similar in business condition and application Output information of the element device sensor that obtains information and obtains information related to the element device constituting the diagnosed elevator (diagnosed system device) and the same as the element device sensor of the diagnosed elevator (diagnosed system device) Elements of the second comparison elevator (second comparison system device) of the same model as the diagnostic elevator (diagnostic system device) The output information of the sensor is compared to obtain the fluctuation information about the operation of the component equipment of the elevator to be diagnosed (diagnostic system equipment), and from the correlation between the behavior information of the user and the fluctuation information of the element equipment, It is configured to determine whether or not an abnormal state has occurred in the system device under diagnosis) due to human factors.

  According to this, the abnormal state occurring in the diagnosed elevator (diagnosed system device) is an abnormal state caused by an artificial cause, or the cause based on the component devices of the diagnosed elevator (diagnosed system device) It is possible to identify at an early stage whether or not the abnormal state has occurred, and it is possible to shorten the stop period of the elevator to be diagnosed (diagnostic system equipment) and improve the convenience for the user.

  In addition, this invention is not limited to an above-described Example, Various modifications are included. For example, the above-described embodiments have been described in detail for easy understanding of the present invention, and are not necessarily limited to those having all the configurations described. Further, a part of the configuration of one embodiment can be replaced with the configuration of another embodiment, and the configuration of another embodiment can be added to the configuration of one embodiment. Further, it is possible to add, delete, and replace other configurations for a part of the configuration of each embodiment.

  DESCRIPTION OF SYMBOLS 1 ... Human sensor, 2 ... Surveillance camera, 3 ... Load sensor, 4 ... Door sensor, 5 ... Sensor A, 6 ... Sensor B, 7 ... Abnormal operation detection means, 8 ... Sensor group of elevator for similar use, 9 ... Normal Time variation information storage means, 10... Fluctuation information detection means, 11... Elevator sensor group of the same model, 12. Secular change information storage means, 13... Origin determination means, 14.

Claims (8)

The output information of the behavior sensor that acquires information on the behavior of the user of the system device to be diagnosed to be diagnosed, and the first comparison system device that is the same as the behavior sensor of the system device to be diagnosed and has similar business conditions and uses A behavior detecting means for obtaining behavior information of a user of the system device to be diagnosed by comparing output information of the behavior sensor;
Output information of an element device sensor that obtains information on element devices constituting the diagnosis system device, and a second comparison system that is the same as the element device sensor of the diagnosis system device and is the same model as the diagnosis system device Fluctuation information detecting means for comparing the output information of the component device sensor of the device to obtain variation information regarding the operation of the component device of the system device to be diagnosed;
Based on the correlation between the user behavior information obtained by the behavior detection means and the fluctuation information of the element device obtained by the fluctuation information detection means, whether an abnormal state has occurred in the diagnostic system device due to an artificial cause An abnormality diagnosing device for a system device, comprising: a cause determining means for determining whether or not. The output information of the behavior sensor that acquires information on the behavior of the user of the diagnosed elevator to be diagnosed, and the behavior sensor of the first comparison elevator that is the same as the behavior sensor of the diagnosed elevator and similar in business condition and application A behavior detecting means for comparing the output information to obtain the behavior information of the user of the diagnosed elevator;
The output information of the element device sensor for acquiring information related to the element device constituting the diagnosed elevator, and the element device of the second comparison elevator that is the same as the element device sensor of the diagnosed elevator and is the same model as the diagnosed elevator Fluctuation information detecting means for comparing the output information of the sensor to obtain fluctuation information related to the operation of the element device of the elevator to be diagnosed;
Whether or not an abnormal state has occurred in the diagnosed elevator due to an artificial cause based on the correlation between the behavior information of the user obtained by the behavior detection means and the fluctuation information of the element device obtained by the fluctuation information detection means An elevator abnormality diagnosing device comprising an origin determining means for determining whether or not. In the elevator abnormality diagnosis device according to claim 2,
The output information of the behavior sensor of the first comparison elevator is output information having a width of a first predetermined range, and the behavior detection means outputs the output information of the behavior sensor of the diagnosed elevator as the first information. If it exceeds the predetermined range, it is judged that there is an abnormal behavior of the user,
The output information of the element device sensor of the second comparison elevator is output information having a width of a second predetermined range, and the variation information detecting means is configured to output the output information of the element device sensor of the diagnosis elevator. If it exceeds the 2nd predetermined range, it will judge that the said element apparatus has abnormality, The abnormality diagnosis apparatus of the elevator characterized by the above-mentioned. In the elevator abnormality diagnosis device according to claim 3,
The cause determination unit is configured to generate an artificial cause for the diagnosed elevator based on the correlation between the degree of behavior information obtained by the behavior detection unit and the degree of variation information obtained by the variation information detection unit. And determining whether or not an abnormal condition has occurred in the elevator. In the elevator abnormality diagnosis device according to claim 3 or claim 4,
When the cause determination unit detects an abnormality of the element device within a predetermined time after detecting an abnormal behavior of the user, the abnormality of the element device generated at this time is caused by the cause of the user An elevator abnormality diagnosis device characterized by determining an abnormal state and determining that the elemental device is abnormal after detecting the abnormality of the elemental device after the predetermined time has elapsed. In the elevator abnormality diagnosis device according to claim 3 or claim 4,
The behavior sensor is a load sensor that detects a user's load existing in a car or a sensor camera that monitors a user's behavior, and the element device sensor is a door sensor that detects the opening / closing speed of a car door. An elevator abnormality diagnosis device characterized by being: In the elevator abnormality diagnosis device according to claim 3 or claim 4,
The variation information detecting means is configured to determine the element of the diagnosed elevator based on time-dependent deterioration information indicating deterioration over time of the element device detected by the element device sensor of the diagnosed elevator and the second comparison elevator. An elevator abnormality diagnosis device characterized in that the degree of deterioration of equipment over time is obtained. The output information of the behavior sensor that acquires information on the behavior of the user of the diagnosed elevator to be diagnosed, and the behavior sensor of the first comparison elevator that is the same as the behavior sensor of the diagnosed elevator and similar in business condition and application Compare the output information to determine the behavior information of the user of the diagnosed elevator,
The output information of the element device sensor for acquiring information related to the element device constituting the diagnosed elevator, and the element device of the second comparison elevator that is the same as the element device sensor of the diagnosed elevator and is the same model as the diagnosed elevator By comparing the output information of the sensor to obtain the variation information regarding the operation of the element device of the diagnosed elevator,
Whether or not an abnormal state has occurred in the diagnosed elevator due to an artificial cause based on the correlation between the behavior information of the user obtained by the behavior detection means and the fluctuation information of the element device obtained by the fluctuation information detection means An elevator abnormality diagnosis method characterized by determining whether or not.

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